Introduction

If you’ve ever dropped a talking avatar into a LiveKit voice agent, you’ve probably seen the same startup pattern: the voice pipeline comes up quickly, but the avatar-facing side still has to initialize video state, negotiate a realtime session, and wait for the first usable mouth movement before anything looks “alive.” That gap is small in absolute terms, but users notice it immediately. It reads as lag, even when audio is already flowing.

This post is about eliminating most of that perceived delay by migrating from on-demand avatar creation to prewarmed avatar sessions. The end goal is simple: when the agent starts speaking, the lip-synced face is already ready to render, so the first utterance lands with a much tighter audio-video start. I’ll focus on the mechanics that matter in production: session lifecycle, warmup strategy, how to wire it into a LiveKit agent, and what trade-offs to watch for.

Why avatar startup is slower than voice startup

A voice agent and a realtime avatar do not “start” in the same way. A voice agent usually needs to:

• join a room or open a websocket/session,

• start VAD, STT, LLM, and TTS pipelines,

• begin streaming audio back to the client.

An avatar pipeline adds extra work on top of that. The system has to establish a video-capable session, allocate render resources, and sync the mouth motion to the speech stream. Depending on how the avatar service is implemented, the first frame may arrive only after the session is fully negotiated and the face is “live.”

For a human user, the important metric is not just time-to-first-audio. It’s time-to-first-coherent presence: the moment the voice and face begin together. If audio appears first and the face catches up a second later, the agent feels asynchronous, even if the gap is technically acceptable.

That’s why prewarming helps. You pay the setup cost before the user is waiting, then attach the live voice session to an already-initialized avatar session when the conversation begins.

What “prewarmed” means in practice

Prewarming is just session pooling with a human-friendly name. The idea is to create an avatar session ahead of time, keep it alive for a short period, and assign it to a user interaction when needed. The session is “warm” because the expensive work is already done:

• the avatar has been created and initialized,

• the realtime transport is established,

• any media pipelines needed for lip-sync are already running.

Then, when your LiveKit agent is ready to speak, you bind that session to the active conversation. The avatar doesn’t have to cold-start in the critical path.

There are a few useful design constraints here:

• Warm sessions should be short-lived. Keep the pool small and expire unused sessions quickly to control cost.

• Sessions should be user-scoped or request-scoped. Don’t reuse the same live avatar session across unrelated conversations.

• Attach as late as possible, but not too late. Warm the session before you know the exact turn, then bind it once the agent is about to produce speech.

In other words, treat the avatar session like any other realtime resource: create ahead of demand, but keep the lifetime tight.

Migration pattern for a LiveKit voice agent

If you already use a LiveKit agent and want to add a synchronized talking face, the migration usually has two phases:

1. split avatar session creation out of the “first user turn” path,

2. connect the prewarmed session to the agent before the first synthesized utterance.

That keeps the speech pipeline fast without making the avatar a blocking dependency.

Step 1: create the session before the conversation starts

A simple approach is to create a warm session when you know a user is likely to join: on page load, on “connect” click, or when your backend allocates a new call. If you’re using the Protoface REST API directly, the exact request shape depends on your avatar/session model, but the flow looks like this:

curl -X POST "https://api.protoface.com/v1/sessions" \
}'

curl -X POST "https://api.protoface.com/v1/sessions" \
}'

curl -X POST "https://api.protoface.com/v1/sessions" \
}'

That returns a session identifier and whatever connection metadata your app needs to keep the session alive or attach it later. Store only what you need server-side. Do not expose API keys in the browser.

Step 2: connect the session to your LiveKit agent

In a typical LiveKit setup, your agent already has a lifecycle hook where it initializes plugins or external media services. That’s the right place to attach the avatar session. The exact API depends on the integration you use, but the pattern is consistent: the avatar plugin gets a session reference, then it forwards speech timing or audio markers so the face can lip-sync to the agent’s output.

With the LiveKit plugin, the code is intentionally lightweight. The important part is that you inject a warmed session instead of creating one inline during the first turn:

from livekit.agents import JobContext

await agent.run(ctx)

from livekit.agents import JobContext

await agent.run(ctx)

from livekit.agents import JobContext

await agent.run(ctx)

The helper names above are illustrative; the actual plugin constructor and session fields are documented in the repo and SDK docs. The key architecture point is that avatar session creation is moved out of the hot path.

Step 3: keep the warmup window short

This part matters more than people expect. If you warm sessions too early, you end up paying for sessions that never get used. If you warm them too late, you’re back where you started.

A practical strategy is:

• create the session when the user intent is high,

• keep a short TTL on the warm pool,

• refresh only if the user is still likely to engage,

• dispose of the session as soon as the conversation ends.

For a support flow, that might mean creating a warm session when the customer opens the help widget. For a live call, it might mean prewarming as soon as you have a room assignment or a queue placement. For a website avatar, it could be on the first interaction that indicates actual engagement rather than idle browsing.

Failure modes and the trade-offs you should expect

Prewarming is not free. It improves perceived latency by moving work earlier, but it also introduces operational choices:

• Resource cost: idle warm sessions consume compute and/or billed usage depending on your quality tier and session policy.

• Stale sessions: if a session sits around too long, it may expire or become disconnected before use.

• Orphaned sessions: if your app creates warm sessions but never attaches them, you’ll leak spend unless you clean them up.

• Retry complexity: if the warm session fails, your agent needs a fallback path that can cold-start a new session or continue without video.

Also be careful about timing expectations. Prewarming improves startup latency, but it does not eliminate network jitter, TTS buffering, or the fact that lip-sync still depends on receiving the first speech frames. If the agent waits a long time before speaking, the avatar may still appear idle. The real win is the first audible turn: the user sees motion immediately when the agent responds.

One subtle point: if your voice model streams partial text quickly but your avatar only animates once TTS begins, make sure your orchestration doesn’t wait on an upstream “completion” event. You usually want the avatar connected before the first TTS chunk is emitted, not after the text is finalized.

How Protoface fits into the migration

This is exactly the sort of workflow Protoface is designed for: you create and manage avatar sessions through the API, then feed that session into your realtime voice stack. For developers already on LiveKit, the relevant surface is the plugin and its examples in the repository, which show how to drop a synchronized talking face into an existing agent without reworking the whole pipeline. Start from the integration guidance in the docs and the plugin code in the GitHub organization if you need a concrete reference for session lifecycle and wiring.

In a production setup, I’d keep the responsibilities clean:

• your backend decides when to warm and retire sessions,

• the agent process consumes a session reference and runs the conversation,

• the avatar layer handles the realtime lip-sync and video delivery.

That separation makes it much easier to test startup behavior, measure time-to-first-face, and fall back gracefully when the avatar service is unavailable.

Conclusion

Moving a LiveKit voice agent to prewarmed avatar sessions is mostly an orchestration change, not a media one. Create the avatar session before the user is waiting, attach it to the agent before the first spoken turn, and keep the warm window short enough that cost stays under control. Done well, it removes the awkward “audio now, face later” effect and makes the whole agent feel significantly more responsive.

If you’re implementing this today, start with a thin backend prewarm flow, then wire the session into your LiveKit plugin path. The docs at docs.protoface.com are the right place to confirm the current API shape, and the quickstart repos linked from the project README are useful if you want a concrete end-to-end example.